class_docstr: Tests the correctness of the Y = g(X) = Log[1 + exp(X)] transformation.
code: def testBijectorForwardInverseWithHingeSoftnessEventDimsZero(self):
      bijector = softplus.Softplus(hinge_softness=np.float64(1.5))
      x = 2 * rng.randn(2, 10)
      y = 1.5 * self._softplus(x / 1.5)
  
      self.assertAllClose(y, self.evaluate(bijector.forward(x)))
      self.assertAllClose(x, self.evaluate(bijector.inverse(y)))
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 6 for calling the SoftplusBijectorTest obj's underlying member method code with example usage: obj.testBijectorForwardInverseWithHingeSoftnessEventDimsZero() without return types